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Deciphering Federal Reserve Communication Via Text Analysis of Alternative FOMC Statements ∗

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Deciphering Federal Reserve Communication Via Text Analysis of Alternative FOMC Statements ∗ Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements ∗ Taeyoung Dohy Dongho Songz Shu-Kuei Yangx October 20, 2020 Abstract We apply a natural language processing algorithm to FOMC statements to construct a new measure of monetary policy stance, including the tone and novelty of a policy statement. We exploit cross-sectional variations across alternative FOMC statements to identify the tone (for example, dovish or hawkish) and contrast the current and previous FOMC statements released after Committee meetings to identify the novelty of the announcement. We then use high-frequency bond prices to compute the surprise component of the monetary policy stance. Our text-based estimates of monetary policy surprises are not sensitive to the choice of bond maturities used in estimation, are highly correlated with forward guidance shocks in the literature, and are associated with lower stock returns after unexpected policy tightening. The key advantage of our approach is that we are able to conduct a counterfactual policy evaluation by replacing the released statement with an alternative statement, allowing us to perform a more detailed investigation at the sentence and paragraph level. JEL Classification: E30, E40, E50, G12. Keywords: Alternative FOMC statements, counterfactual policy evaluation, monetary policy stance, text analysis, natural language processing. ∗We thank John Rogers for sharing his dataset on monetary policy shock estimates with us. We thank Kyungmin Kim and Suyong Song as well as seminar participants at the Federal Reserve Bank of Kansas City, the Interfed Brown Bag, the KAEA webinar, and National University of Singapore who provided helpful comments. All the remaining errors are ours. The opinions expressed herein are those of the authors and do not reflect the views of the Federal Reserve Bank of Kansas City or Federal Reserve System. yFederal Reserve Bank of Kansas City; [email protected] zCarey Business School, Johns Hopkins University; [email protected] xFederal Reserve Bank of Kansas City; [email protected] 1 1 Introduction Central banks have increasingly relied on public communications to provide guidance re- garding future policy actions, e.g., Woodford(2005) and Blinder et al.(2008). The practice became more prevalent when monetary policy is constrained by the effective lower bound, see Bernanke(2010). In this regard, both quantitative decisions (e.g., interest rates or asset purchases) made by central banks and their qualitative descriptions of the economic fac- tors that lead to the decisions serve as important information variables for understanding monetary policy. While the profession has moved toward treating policy statements and speeches by central bank officials as data to be analyzed, important limitations exist in the parsing of textual content. First, quantifying the tone (between a dovish stance and a hawkish one) automat- ically from only the publicly released text is difficult because the tone is often relative to what could have been released. Second, even when using asset prices as instruments, identi- fying which part of a communication is perceived as most crucial by the markets is difficult to do because texts like statements and speeches are multi-dimensional objects. In addi- tion, assumptions are required on the maturity structure of bond prices to translate bond price movements into particular parts of the statement. Third, it is hard to evaluate the (counterfactual) impact of alternative language in the statement on the markets within the commonly used text analysis methods largely based on word counting when the alternative language mainly changes the contextual meaning of words rather than the frequency pattern of words. The purpose of this paper is to contribute in all three dimensions to enhance our under- standing of the transmission of monetary policy to the financial markets, which is important for both policy makers and market participants. We work with the Federal Open Market Committee's (FOMC) post-meeting statements in this paper. We differ from the existing literature on two fronts in achieving our objectives. First, we refine the information in the FOMC statements using a novel natural language processing algorithm known as the Universal Sentence Encoding (USE). In contrast to the word-counting methods that ignore the local context (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA)), the USE provides context-aware representation of words in the document. Second, and more importantly, we consider alternative policy statements created 2 by the staff of the Federal Reserve Board as well as the statement released right after the meeting in parsing of policy statements. Alternative statements are available for each FOMC meeting since March 2004 and contain a more dovish (Alt A) or a more hawkish (Alt C or Alt D) statement than the benchmark one (Alt B). The different views of economic outlook and policy prescription contained in the alternative statements provide important anchoring points for interpreting the tone of the policy statement released after the meeting.1 We provide a novel measure of the surprise component of monetary policy announcements based on our text analysis. We do this in two steps. First, we characterize the \monetary policy stance" communicated by each FOMC statement. For this, we identify the \tone" of monetary policy announcements by computing the similarities in terms of the USE rep- resentation between the released statement and the alternative statements.2 We define the \novelty" of monetary policy announcements by computing the distance in terms of the USE representation between the current statement and the previous statement. By taking the product of the tone and novelty of monetary policy announcements, we obtain the monetary policy stance. The first step only relies on the text analysis of the FOMC statements. In the second step, we introduce (high-frequency) bond prices to compute the surprise com- ponent of the monetary policy stance. The expected component of the monetary policy stance is defined as a weighted average of the hawkish and dovish policy stances of the al- ternative statements. We back out the weight that maximizes the rank correlation of the (high-frequency) bond prices and the surprise component of monetary policy announcements (aka, monetary policy shocks), namely, the difference between the monetary policy stance and its expected component. We then use (high-frequency) stock prices to show that a tightening policy surprise ac- cording to our measure generates a negative stock price reaction. Specifically, a positive one-standard-deviation surprise leads to a 20 basis points (bps) drop in stock prices on average. Also, we verify that our measure of monetary policy shocks is highly correlated (about 50%) with forward guidance shocks identified in the existing literature (that relies 1Like the FOMC transcripts, alternative statements are made public five years after they were created. Although published with a significant lag, dovish and hawkish alternative statements generally incorporate information on market expectations regarding the upcoming policy statement because they are written to surprise the market in respective directions. See FRB(2004) for the detailed description of statement language. 2The released statement is typically very similar to the benchmark statement created before each FOMC meeting but may not be exactly same because some phrases can change during the FOMC deliberation. 3 on high-frequency bond prices). Both serve as external validation of our measure. The key advantage of our approach is that we are able to conduct a counterfactual policy evaluation by replacing the released statement with either one of the alternative statements. Specifically, we work with FOMC statements released in September 2007 and December 2010 as two illustrative examples.3 The post-meeting statement in September 2007 lowered the federal funds rate target by 50 bps but it noted that \the Committee judges that some inflation risks remain, and it will continue to monitor inflation developments carefully." We consider the counterfactual statement that would lower the federal funds rate target by 25 bps but mention \the downside risks to economic growth outweigh the upside risks to inflation.” In spite of a smaller rate cut, we find that this counterfactual statement might be a more dovish statement, leading to an 1.94% increase of stock prices instead of the 0.1% increase after the release of the post-meeting statement.4 Another example is the December 2010 FOMC meeting in which the counterfactual statement suggests a more explicit time- dependent forward guidance on the future path of the federal funds rate by replacing \for an extended period" by \at least through the mid-2012' to indicate how long the interest rate would stay low. It also announces additional asset purchases. The actual released statement omits such an explicit forward guidance and the expansion of asset purchases. We find that adopting the counterfactual in December 2010 would lead to about 0.77% increase in the stock market return instead of 0.05% increase after the release of the post-meeting statement. These two episodes highlight the importance of narrative information in the Fed communication, which is hard to measure without using text-analysis tools. Related Literature. Our paper is related to multiple lines of research. First, our work draws on papers that identify monetary policy shocks using high-frequency bond data, (e.g., G¨urkaynak et al.(2005), Swanson(2017), Nakamura and Steinsson(2018), Bu et al.(2020), Bauer and Swanson(2020), Hoesch et al.(2020)). Our empirical finding on the negative stock market response to an unexpected monetary policy tightening is consistent with Bu et 3See the appendix for the detailed description of alternative statements. 4We regress stock returns on our measure of monetary policy surprises and obtain the respective OLS coefficient estimates. We replace the monetary policy stance with the counterfactual one and subtract the bond price-implied expected monetary policy stance to compute the counterfactual monetary policy surprise component.
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